My name is Ying and I make websites with interactive graphics !!!
set.seed(1)
data(nyc_airbnb)
nyc_airbnb =
nyc_airbnb %>%
mutate(rating = review_scores_location / 2) %>%
select(
neighbourhood_group, neighbourhood, rating, price, room_type, lat, long) %>%
filter(
!is.na(rating),
neighbourhood_group == "Manhattan",
room_type == "Entire home/apt",
price %in% 100:500) %>%
sample_n(5000)
# ~ is just tell it is a variable
nyc_airbnb %>%
mutate(text_label = str_c("Price: $", price, '\nRating: ', rating)) %>%
plot_ly(
x = ~lat, y = ~long, type = "scatter", mode = "markers",
color = ~price, text = ~text_label, alpha = 0.5)
# the bar chat has to create a dataset for the Y axis to get to the number
common_neighborhoods =
nyc_airbnb %>%
# make each bar the Y score
count(neighbourhood, sort = TRUE) %>%
top_n(8) %>%
select(neighbourhood)
## Selecting by n
inner_join(nyc_airbnb, common_neighborhoods, by = "neighbourhood") %>%
# reorder of factor with same neighbourhood, but increasing price
mutate(neighbourhood = fct_reorder(neighbourhood, price)) %>%
plot_ly(y = ~price, color = ~neighbourhood, type = "box",
colors = "Set2")
#Rcolorbrewer
# Figure 3: not just mean and sd, use the spread of the data, to see if the data is skewed or etc... use the existing dataset, not change anything
# juse patch
# don't just use the dashboard for self use only, more deliverable for collaborators to see the data
nyc_airbnb %>%
count(neighbourhood) %>%
mutate(neighbourhood = fct_reorder(neighbourhood, n)) %>%
plot_ly(x = ~neighbourhood, y = ~n, color = ~neighbourhood, type = "bar")
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors